A Keyword Selection Strategy for Dialogue Move Recognition and Multi-Class Topic Identification
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
OCFS: optimal orthogonal centroid feature selection for text categorization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Angular measures for feature selection in text categorization
Proceedings of the 2006 ACM symposium on Applied computing
A document-centric approach to static index pruning in text retrieval systems
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic document organization in a p2p environment
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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When building document categorization in distributed mobile environments, feature selection methods need to be employed to have a compact representation for each document topic and to reduce noise during classification. When interaction occurs between the nodes, locally retrieved features representing the document topic and their attributes have to be shared to have a more accurate estimation of the global classifier at every node. The network traffic should be kept at a minimum to reduce costs. We propose a probabilistic model for a keyword selection method, which makes a more thorough analysis possible and can be considered as a basis when sharing information. It can be used for building up the local document topic representations incrementally ensuring minimal network traffic. The description of the probabilistic model is complemented by experimental results.